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Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications to Human Settlement Modelling.- [electronic resource]
Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications to Human Settlement Modelling.- [electronic resource]
상세정보
- 자료유형
- 학위논문(국외)
- 자관 청구기호
- 기본표목-개인명
- 표제와 책임표시사항
- Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications to Human Settlement Modelling. - [electronic resource] / Uhl, Johannes Hermann.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource(264 p.)
- 일반주기
- Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
- 일반주기
- Advisor: Leyk, Stefan.
- 학위논문주기
- Thesis (Ph.D.)--University of Colorado at Boulder, 2019.
- 이용제한주기
- This item must not be sold to any third party vendors.
- 요약 등 주기
- 요약Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 81-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 원문정보보기
MARC
008200317s2019 ulk s 00 eng■001000015492239
■00520200217181612
■007cr
■020 ▼a9781088311332
■040 ▼d225006
■08204▼a550
■090 ▼a전자도서(박사논문)
■1001 ▼aUhl, Johannes Hermann.
■24510▼aSpatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications to Human Settlement Modelling.▼h[electronic resource]▼cUhl, Johannes Hermann.
■260 ▼a[S.l.]▼bUniversity of Colorado at Boulder. ▼c2019
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2019
■300 ▼a1 online resource(264 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 81-04, Section: B.
■500 ▼aAdvisor: Leyk, Stefan.
■5021 ▼aThesis (Ph.D.)--University of Colorado at Boulder, 2019.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aDue to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.
■650 4▼aComputer science.
■650 4▼aRemote sensing.
■650 4▼aGeographic information science.
■650 4▼aGeodetics.
■71020▼aUniversity of Colorado at Boulder▼bGeography.
■7730 ▼tDissertations Abstracts International▼g81-04B.
■773 ▼tDissertation Abstract International
■791 ▼aPh.D.
■792 ▼a2019
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T15492239▼nKERIS
